YOLOV3

Introduction

This is my own YOLOV3 written in pytorch, and is also the first time i have reproduced a object detection model.The dataset used is PASCAL VOC. The eval tool is the voc2010. Now the mAP gains the goal score.

Subsequently, i will continue to update the code to make it more concise , and add the new and efficient tricks.

Note : Now this repository supports the model compression in the new branch model_compression

Results

name	Train Dataset	Val Dataset	mAP(others)	mAP(mine)	notes
YOLOV3-448-544	2007trainval + 2012trainval	2007test	0.769	0.768 \| -	baseline(augument + step lr)
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.793	0.803 \| -	+multi-scale training
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.806	0.811 \| -	+focal loss(note the conf_loss in the start is lower)
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.808	0.813 \| -	+giou loss
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.812	0.821 \| -	+label smooth
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.822	0.826 \| -	+mixup
YOLOV3-*-544	2007trainval + 2012trainval	2007test	0.833	0.832 \| 0.840	+cosine lr
YOLOV3--	2007trainval + 2012trainval	2007test	0.858	0.858 \| 0.860	+multi-scale test and flip, nms threshold is 0.45

Note :

YOLOV3-448-544 means train image size is 448 and test image size is 544. "*" means the multi-scale.
mAP(mine)'s format is (use_difficult mAP | no_difficult mAP).
In the test, the nms threshold is 0.5(except the last one) and the conf_score is 0.01.others nms threshold is 0.45(0.45 will increase the mAP)
Now only support the single gpu to train and test.

Environment

Nvida GeForce RTX 2080 Ti
CUDA10.0
CUDNN7.0
ubuntu 16.04

python 3.5

# install packages
pip3 install -r requirements.txt --user

Brief

[x] Data Augment (RandomHorizontalFlip, RandomCrop, RandomAffine, Resize)
[x] Step lr Schedule
[x] Multi-scale Training (320 to 640)
[x] focal loss
[x] GIOU
[x] Label smooth
[x] Mixup
[x] cosine lr
[x] Multi-scale Test and Flip

Prepared work

1、Git clone YOLOV3 repository

git clone https://github.com/Peterisfar/YOLOV3.git

update the "PROJECT_PATH" in the params.py.

2、Download dataset

Download Pascal VOC dataset : VOC 2012_trainval 、VOC 2007_trainval、VOC2007_test. put them in the dir, and update the "DATA_PATH" in the params.py.
Convert data format : Convert the pascal voc *.xml format to custom format (Image_path0 xmin0,ymin0,xmax0,ymax0,class0 xmin1,ymin1...)

cd YOLOV3 && mkdir data
cd utils
python3 voc.py # get train_annotation.txt and test_annotation.txt in data/

3、Download weight file

Darknet pre-trained weight : darknet53-448.weights
This repository test weight : best.pt

Make dir weight/ in the YOLOV3 and put the weight file in.

Train

Run the following command to start training and see the details in the config/yolov3_config_voc.py

WEIGHT_PATH=weight/darknet53_448.weights

CUDA_VISIBLE_DEVICES=0 nohup python3 -u train.py --weight_path $WEIGHT_PATH --gpu_id 0 > nohup.log 2>&1 &

Notes:

Training steps could run the "cat nohup.log" to print the log.
It supports to resume training adding --resume, it will load last.pt automaticly.

Test

You should define your weight file path WEIGHT_FILE and test data's path DATA_TEST

WEIGHT_PATH=weight/best.pt
DATA_TEST=./data/test # your own images

CUDA_VISIBLE_DEVICES=0 python3 test.py --weight_path $WEIGHT_PATH --gpu_id 0 --visiual $DATA_TEST --eval

The images can be seen in the data/

TODO

[ ] Mish
[ ] OctConv
[ ] Custom data

Reference

tensorflow : https://github.com/Stinky-Tofu/Stronger-yolo
pytorch : https://github.com/ultralytics/yolov3
keras : https://github.com/qqwweee/keras-yolo3

Peterisfar / YOLOV3

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